Refining search results

ABSTRACT

A computer-implemented method for processing query information includes receiving data representative of a search query from a user search session. The method also includes identifying a plurality of search results based upon the search query. Each search result is associated with a plurality of user characteristics and data that represents requestor behavior relative to previously submitted queries associated with the respective search result. The method also includes ordering the plurality of user characteristics based upon the data that represents requestor behavior relative to previously submitted queries and the respective search result. The method also includes adjusting the ordered plurality of user characteristics based upon at least one predefined compatibility associated with the user characteristics. The method also includes ranking the search results based upon the adjusted plurality of user characteristics.

BACKGROUND

The present disclosure relates to using data that represents previouslysubmitted user queries to adjust search results.

Internet search engines aim to identify documents or other items thatare relevant to a user's needs and to present the documents or items ina manner that is most useful to the user. Such activity often involves afair amount of mind-reading—inferring from various clues what the userwants. Certain clues may be user specific. For example, knowledge that auser is making a request from a mobile device, and knowledge of thelocation of the device, can result in much better search results forsuch a user.

Clues about a user's needs may also be more general. For example, searchresults can have an elevated importance, or inferred relevance, if anumber of other search results link to them. Such an approach todetermining relevance may be premised on the assumption that, if authorsof web pages felt that another web site was relevant enough to be linkedto, then web searchers would also find the site to be particularlyrelevant. In short, the web authors “vote up” the relevance of thesites.

Other various inputs may be used instead of, or in addition to, suchtechniques for determining and ranking search results. For example, userreactions to particular search results or search result lists may begauged, so that results on which users often click will receive a higherranking. The general assumption under such an approach is that searchingusers are often the best judges of relevance, so that if they select aparticular search result, it is likely to be relevant, or at least morerelevant than the presented alternatives.

SUMMARY

This disclosure describes systems, methods and apparatus includingcomputer program products for refining search results. In general, oneor more aspects of the subject matter described in this specificationcan be embodied in one or more methods for processing query information.The methods include receiving data representative of a search query froma user search session. The methods also include identifying a pluralityof search results based upon the search query. Each search result isassociated with a plurality of user characteristics and data thatrepresents requestor behavior relative to previously submitted queriesassociated with the respective search result. The methods also includeordering the plurality of user characteristics based upon the data thatrepresents requestor behavior relative to previously submitted queriesand the respective search result. The methods also include adjusting theordered plurality of user characteristics based upon at least onepredefined compatibility associated with the user characteristics. Themethods also include ranking the search results based upon the adjustedplurality of user characteristics.

These and other embodiments can optionally include one or more of thefollowing features. One or more of the predefined compatibilities maydefine two or more user languages as being compatible. Predefinedcompatibilities may also define two or more user locations as beingcompatible. Ordering the plurality of user characteristics may includecomputing, for each user characteristic, a fraction based upon the datathat represents requestor behavior relative to previously submittedqueries and the respective search result. Adjusting the orderedplurality of user characteristics may include reordering the pluralityof user characteristics based upon a fraction that is based upon thedata that represents requestor behavior relative to previously submittedqueries and the respective search result. Adjusting the orderedplurality of user characteristics may include removing data associatedwith a user characteristic. Ranking the search results may includecomputing a score for each search result based upon the adjustedplurality of user characteristics. Adjusting the ordered plurality ofuser characteristics may include applying a weight to data thatrepresents requestor behavior relative to previously submitted queriesand the respective search result.

In general, one or more other aspects of the subject matter described inthis specification can be embodied in one or more methods for processingquery information. The methods include receiving data representative ofa search query from a user search session. The methods also includeidentifying a plurality of search results based upon the search query.Each search result is associated with data that represents requestorbehavior relative to previously submitted queries. The requestorbehavior is associated with a plurality of languages used at a locationof the user. For each search result, the methods include combining thedata that represents requestor behavior that is associated the pluralityof languages used at user location. The methods also include ranking thesearch results based upon the combined data that represents requestorbehavior.

These and other embodiments can optionally include one or more of thefollowing features. Combining the data that represents requestorbehavior may include applying a weight to the data that representsrequestor behavior relative to previously submitted queries for theplurality of languages. The location of the user may be associated witha country.

Particular embodiments of the described subject matter can beimplemented to realize one or more of the following advantages. Bydefining particular user characteristics (e.g., user languages, userlocations, etc.) as being compatible, information associated withprevious searches may be exploited to accentuate or downplay therankings of search results.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages will become apparent from the description, thedrawings, and the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example information retrieval system.

FIG. 2 shows example components of an information retrieval system.

FIG. 3 shows another example information retrieval system andcomponents.

FIG. 4 illustrates refining search results.

FIGS. 5 and 6 show flowcharts that represent operations of a rankrefiner engine.

FIG. 7 shows a schematic diagram of an example computer system.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 shows an example system 1000 for improving the relevance ofresults obtained from submitting search queries as can be implemented inan internet, intranet, or other client/server environment. The system1000 is an example of an information retrieval system in which thesystems, components and techniques described below can be implemented.Although several components are illustrated, there may be fewer or morecomponents in the system 1000. Moreover, the components can bedistributed on one or more computing devices connected by one or morenetworks or other suitable communication mediums.

A user 1002 (1002 a, 1002 b, 1002 c) can interact with the system 1000through a client device 1004 (1004 a, 1004 b, 1004 c) or other device.For example, the client device 1004 can be a computer terminal within alocal area network (LAN) or wide area network (WAN). The client device1004 can include a random access memory (RAM) 1006 (or other memoryand/or a storage device) and a processor 1008. The processor 1008 isstructured to process instructions within the system 1000. In someimplementations, the processor 1008 is a single-threaded processor. Inother implementations, the processor 1008 is a multi-threaded processor.The processor 1008 can include multiple processing cores and isstructured to process instructions stored in the RAM 1006 (or othermemory and/or a storage device included with the client device 1004) todisplay graphical information for a user interface.

A user 1002 a can connect to a search engine 1030 within a server system1014 to submit a query 1015. When the user 1002 a submits the query 1015through an input device attached to a client device 1004 a, aclient-side query signal 1010 a is sent into a network 1012 and isforwarded to the server system 1014 as a server-side query signal 1010b. Server system 1014 can be one or more server devices in one or morelocations. The server system 1014 includes a memory device 1016, whichcan include the search engine 1030 loaded therein. A processor 1018 isstructured to process instructions within the system 1014. Theseinstructions can implement one or more components of the search engine1030. The processor 1018 can be a single-threaded processor or amulti-threaded processor, and can include multiple processing cores. Theprocessor 1018 can process instructions stored in the memory 1016related to the search engine 1030 and can send information to the clientdevice 1004, through the network 1012, to create a graphicalpresentation in a user interface of the client device 1004 (e.g., asearch results web page displayed in a web browser).

The server-side query signal 1010 b is received by the search engine1030. The search engine 1030 uses the information within the user query1015 (e.g. query terms) to find relevant documents. The search engine1030 can include an indexing engine 1020 that actively searches a corpus(e.g., web pages on the Internet) to index the documents found in thatcorpus, and the index information for the documents in the corpus can bestored in an index database 1022. This index database 1022 can beaccessed to identify documents related to the user query 1015. Notethat, an electronic document (which for brevity will simply be referredto as a document) does not necessarily correspond to a file. A documentcan be stored in a portion of a file that holds other documents, in asingle file dedicated to the document in question, or in multiplecoordinated files.

The search engine 1030 can include a ranking engine 1052 to rank thedocuments related to the user query 1015. The ranking of the documentscan be performed using traditional techniques for determining aninformation retrieval (IR) score for indexed documents in view of agiven query. The relevance of a particular document with respect to aparticular search term or to other provided information may bedetermined by any appropriate technique. For example, the general levelof back-links to a document that contains matches for a search term maybe used to infer a document's relevance. In particular, if a document islinked to (e.g., is the target of a hyperlink) by many other relevantdocuments (e.g., documents that also contain matches for the searchterms), it can be inferred that the target document is particularlyrelevant. This inference can be made because the authors of the pointingdocuments presumably point, for the most part, to other documents thatare relevant to their audience.

If the pointing documents are in turn the targets of links from otherrelevant documents, they can be considered more relevant, and the firstdocument can be considered particularly relevant because it is thetarget of relevant (or even highly relevant) documents. Such a techniquemay be the determinant of a document's relevance or one of multipledeterminants. The technique is exemplified in some systems that treat alink from one web page to another as an indication of quality for thelatter page, so that the page with the most such quality indicators israted higher than others. Appropriate techniques can also be used toidentify and eliminate attempts to cast false votes so as toartificially drive up the relevance of a page.

To further improve such traditional document ranking techniques, theranking engine 1052 can receive an additional signal from a rankmodifier engine 1056 to assist in determining an appropriate ranking forthe documents. The rank modifier engine 1056 provides one or more priormodels, or one or more measures of relevance for the documents based onone or more prior models, which can be used by the ranking engine 1052to improve the search results' ranking provided to the user 1002. Ingeneral, a prior model represents a background probability of documentresult selection given the values of multiple selected features, asdescribed further below. The rank modifier engine 1056 can perform oneor more of the operations to generate the one or more prior models, orthe one or more measures of relevance based on one or more prior models.

Various types of information may be provided to the rank modifier engine1056 for improving the ranking of documents. For example, informationassociated the search requester may be identified and used to adjustrankings of search results. Such information may include the languageused by the search requester, the geographic location of the searchrequester, etc. Similar information associated with previous searchrequesters may also be used to adjust rankings. To identify such searchrequester information, and accordingly adjust rankings based on theinformation, the search engine 1030 can include a rank refiner engine1058 that may implement one or more adjustment techniques. In onearrangement the rank modifier engine 1056 may modify rankings based onone of three distinct levels. At the first level, the rank modifierengine 1056 may rely upon information from similar queries previouslyinitiated by search requesters using the same language and from the samegeneral location (e.g., country) as the current search requester. Ifsuch information is lacking, the rank modifier 1056 may attempt tomodify rankings at a second level. In one arrangement, the second levelmay allow additional information from previous searches to be used. Forexample, the second level may use information from similar queriespreviously initiated by search requesters that use the same language asthe current search requester, but independent of the location of theprevious search requesters. As such, additional useful information maybecome available by expanding the pool of previous search information.If a statistically significant amount of information is still absent,the rank modifier engine 1056 may further expand the pool to includedata from all previous searches that were similar to the current search.As such, search information independent of search requester language andlocation may be used.

In some respects, such a three-level arrangement allows the rankmodifier 1056 to exploit information shared by search requesters forranking search results. However, by moving from one level to the next insuch abrupt steps, other types of similarities among search requests maynot be exploited. For example, similarities associated with userlanguages (e.g., Russian and Ukrainian) may be used for ranking searchresults. By recognizing the similarities in particular languages,information associated from previous searches for both languages may beexploited. For example, the rank modifier 1056 may be unable to identifyprevious search requests provided in Russian that are similar to acurrent Russian search request. As such, the rank modifier 1056 mayelect to open the search pool to include previous searches independentof language. As such, some previous search requests in Ukrainian, whichis similar to Russian, may be applied a similar weight (even if highlyrelevant) to another language (e.g., Chinese) that is very differentfrom Russian, but for which a significant amount of search requests mayexist. By taking language similarities into account, the rank refinerengine 1058 may allow previous search information to be exploited, whichmay provide more relevant result rankings for the search requester.Similar to accounting for language similarities such as compatibility,the rank refiner engine 1058 may also exploit other similaritiesassociated with searchers. For example, similarities associated withsearcher locations (e.g., country of the searcher) may be considered. Inone some arrangements, similar geographical locations (e.g., borderingcountries such as Mexico, Canada, the United States, etc.), locationswith common culture and climate (e.g., northern African countries), orother types of similarities may be used for adjusting the rankings ofsearch results. Further, such language and location similarities may beused to define additional levels that may be inserted between somewhatabrupt levels (e.g., between a common language level and a languageindependent level). To identify such similarities (e.g., compatiblelanguages, similar locations), various types of information may be used.For example, search requester characteristics (e.g., language, location,etc.) may be provided by the search requestor (e.g., language used toprovide a search request), or may be inferred by analyzing data relatedto the search request or a series of search requests, or determined insome other manner. Additionally, information related to interactionsbetween the search requestor and search results (e.g., click data,refining queries, etc.) may be used. Data representing the similaritiesmay be cataloged in a database (e.g., the index db 1022). From theinformation, search result scoring and ranking (e.g., as performed bythe ranking engine 1052 and the rank modifier engine 1056) can beadjusted to account for such language, location and other types ofsimilarities.

The search engine 1030 can forward the final, ranked result list withina server-side search results signal 1028 a through the network 1012.Exiting the network 1012, a client-side search results signal 1028 b canbe received by the client device 1004 a where the results can be storedwithin the RAM 1006 and/or used by the processor 1008 to display theresults on an output device for the user 1002 a.

FIG. 2 shows example components of an information retrieval system.These components can include an indexing engine 2010, a scoring engine2020, a ranking engine 2030, a rank modifier engine 2070, and a rankrefiner engine 2080. The indexing engine 2010 can function as describedabove for the indexing engine 1020. In addition, the scoring engine 2020can generate scores for document results based on many differentfeatures, including content-based features that link a query to documentresults, and query-independent features that generally indicate thequality of document results. The content-based features can includeaspects of document format, such as query matches to title or anchortext in an HTML (Hyper Text Markup Language) page. The query-independentfeatures can include aspects of document cross-referencing, such as arank of the document or the domain. Moreover, the particular functionsused by the scoring engine 2020 can be tuned, to adjust the variousfeature contributions to the final IR score, using automatic orsemi-automatic processes.

The ranking engine 2030 can produce a ranking of document results 2040for display to a user based on IR scores received from the scoringengine 2020 and one or more signals from the rank modifier engine 2070.The rank modifier engine 2070 can adjust rankings at least in part basedon data received from the rank refiner engine 2080. Along with beingprovided data from the result selection logs 2060, other sources mayprovide information to the rank refiner engine 2080. For example,queries entered into a user interface may be provided to the rankrefiner engine 2080. In this particular example, the rank refiner engine2080 provides information to the rank modifier engine 2070 for rankingadjustments, however other architectures may be implemented. Forexample, information may be provided by the rank refiner engine 2080 tothe indexing engine 2010 or one or more other components of theinformation retrieval system. A tracking component 2050 can be used torecord information regarding individual user selections of the resultspresented in the ranking 2040. For example, the tracking component 2050can be embedded JavaScript code included in a web page ranking 2040 thatidentifies user selections (clicks) of individual document results andalso identifies when the user returns to the results page, thusindicating the amount of time the user spent viewing the selecteddocument result. In other implementations, the tracking component 2050can be a proxy system through which user selections of the documentresults are routed, or the tracking component can include pre-installedsoftware at the client (e.g., a toolbar plug-in to the client'soperating system). Other implementations are also possible, such as byusing a feature of a web browser that allows a tag/directive to beincluded in a page, which requests the browser to connect back to theserver with message(s) regarding link(s) clicked by the user.

The recorded information can be stored in the result selection log(s)2060. The recorded information can include log entries that indicate,for each user selection, the query (Q), the document (D), the time (T)on the document, the language (L) employed by the user, and the country(C) where the user is likely located (e.g., based on the server used toaccess the IR system). Other information can also be recorded regardinguser interactions with a presented ranking, including negativeinformation, such as the fact that a document result was presented to auser, but was not clicked, position(s) of click(s) in the userinterface, IR scores of clicked results, IR scores of all results shownbefore the clicked result, the titles and snippets shown to the userbefore the clicked result, the user's cookie, cookie age, IP (InternetProtocol) address, user agent of the browser, etc. Still furtherinformation can be recorded, such as described below during discussionof the various features that can be used to build a prior model.Moreover, similar information (e.g., IR scores, position, etc.) can berecorded for an entire session, or multiple sessions of a user,including potentially recording such information for every click thatoccurs both before and after a current click.

The information stored in the result selection log(s) 2060 can be usedby one or more components of the information retrieval system. Forexample, information could be provided to the rank refiner engine 2080and the rank modifier engine 2070 in generating the one or more signalsto the ranking engine 2030. In general, a wide range of information canbe collected and used to modify or tune the click signal from the userto make the signal, and the future search results provided, a better fitfor the user's needs. Thus, user interactions with the rankingspresented to the users of the information retrieval system can be usedto improve future rankings. Additionally, language and locationsimilarities associated with the search requester can be used to adjustrankings. In some arrangements, the user interaction and the datarepresenting language and location similarities may be provided to oneor more server systems (e.g., server system 1014) for use and storage(e.g., database 1022) for later retrieval.

The components shown in FIG. 2 can be combined in various manners andimplemented in various system configurations. For example, the scoringengine 2020 and the ranking engine 2030 can be merged into a singleranking engine, such as the ranking engine 1052 of FIG. 1. The rankrefiner engine 2080, the rank modifier engine 2070 and the rankingengine 2030 can also be merged, and in general, a ranking engineincludes any software component that generates a ranking of documentresults after a query. Moreover, a ranking engine can be included in aclient system in addition to (or rather than) in a server system.

FIG. 3 shows another example information retrieval system. In thissystem, a server system 3050 includes an indexing engine 3060 and ascoring/ranking engine 3070. A client system 3000 includes a userinterface for presenting a ranking 3010, a tracking component 3020,result selection log(s) 3030 and a ranking/rank modifier engine/rankrefiner engine 3040. For example, the client system 3000 can include acompany's enterprise network and personal computers, in which a browserplug-in incorporates the ranking/rank modifier engine/rank refinerengine 3040. When an employee in the company initiates a search on theserver system 3050, the scoring/ranking engine 3070 can return thesearch results along with either an initial ranking or the actual IRscores for the results. The browser plug-in can then re-rank the resultslocally based on tracked page selections for the company-specific userbase.

Referring to FIG. 4, an exemplary set of search result information isused to illustrate operations of the rank refiner engine 1058. In thisexample, the operations are used to define a level that accounts forlanguage similarities. For example, a search requester may be an Englishspeaking individual that is from Germany. As mentioned, the language ofthe requester and the associated country of the requester may beidentified from input information (e.g., search language used by therequester, etc.). Based upon the search request entered by therequester, a collection of documents are identified as possible searchresults that may provide the requester with sought after information. Assuch, the provided search query can be paired with each identifieddocument. In this example, the entered query produces a collection ofdocuments in a variety of languages. To exploit language similarities,the rank refiner engine 1058 processes the document collection basedupon the languages detected for each query and document pair. In thisillustration, for one query/document pair, a collection of language data400 is associated with four languages (e.g., English 402, German 404,Spanish 406 and Chinese 408). For each identified language,corresponding click data is also identified for this query/documentpair. From this click data, the rank refiner engine 1058 produces ametric for comparing use of the document for each language. In thisarrangement, a fraction (referred to as a click fraction) is calculatedfor each of the identified languages. Various types of fractions may bedefined and used by the rank refiner engine 1058, for example, the clickfraction may represent a ratio between the number of instances that thedocument was selected (for that respective language) and the totalnumber of instances that the document was selected (for all of thelanguages). In this example, a collection of click fractions 410 ispresented adjacent to the corresponding language collection 410 (e.g.,click fraction 412 for English 402, click fraction 414 for German 404,click fraction 416 for Spanish 406, and click fraction 418 for Chinese408).

Once the collection of click fractions 410 are calculated for eachlanguage, the rank refiner engine 1058 orders the languages included inthe collection 410 based upon the values of the click fractions. In thisexample, the click fractions are ordered in a descending manner and arepresented by language collection 420 and corresponding click fractioncollection 422. As indicated by the collections 420, 422, the clickfraction associated with the Chinese language is the largest followed bythe English, German and Spanish languages. The click data demonstrates astrong relationship between this query/document pair and the Chineselanguage, however, the Chinese language and the language of the queryrequestor (i.e., the English language) may be considered dissimilar, andsome may even consider the two languages as being incompatible. As such,the click data associated with the Chinese language (from thequery/document pair) may skew ranking results.

To account for language differences, the rank refiner engine 1058 mayadjust the language collection 420 and the corresponding click datacollection 422 to separate languages considered incompatible (with thelanguage of the requester) and languages considered compatible (with thelanguage of the requester). In this example, the rank refiner engine1058 produces language collection 424 and corresponding click fractioncollection 426. As illustrated, languages considered incompatible (e.g.,Chinese language 408) by the rank refiner engine 1058 are placed at thehead of the language collection 424 and languages considered compatible(e.g., English 402, German 404 and Spanish 406) are positioned towardthe tail of the language collection 424. The rank refiner engine 1058correspondingly adjusts the click fraction information to produce clickfraction collection 426.

Along with re-arranging the language data based upon compatibility, therank refiner engine 1058 also adjusts the language data. In thisexample, the rank refiner engine 1058 has identified an incompatiblelanguage (e.g., Chinese) with a considerable click fraction. To reducethe effects of this incompatible language click fraction (e.g., ondocument scoring and ranking), the rank refiner engine 1058 may adjustthe click fraction or remove the language entry in its entirety. Forexample, one or more weights may be applied to the click fractions suchthat the significance of incompatible languages is noticeably reduced.References to incompatible languages (and associated data), as definedby the rank refiner engine 1058, may also be removed from thecollections. In this particular example, the rank refiner engine 1058identifies the incompatible languages at the head of the languagecollection 424 and removes the identified languages from the collectionalong with the corresponding click fraction information from the clickfraction collection 426. As indicated by brackets 428, for this example,the information associated with the incompatible language (Chinese) isremoved from both collections 424 and 426. In some arrangements, othertechniques may be used to identify information for removal. For example,click fractions for one or a few languages may be significantlystatistically different (e.g., larger) from click fractions associatedwith the majority of other languages. Based upon this significantdifference in click fraction, the language (or languages) associatedwith the outlier click fractions may be removed from the languagecollection 424.

Once the incompatible language information has been removed, collectioninformation of a reduced size remains. In this example, a languagecollection 430 and a corresponding click fraction collection 432 withthree entries each remain. Similar to the previous collections, for thisquery/document pair, the number of instances that the document has beenselected (or another type of click data) for each language and the totalnumber of instances that the document has been selected is determined bythe rank refiner engine 1058. From the data associated with remaininglanguages, the rank refiner engine 1058 re-orders the languages for thisquery/document pair. For example, click fractions may be re-calculatedfor each of the remaining languages, and the languages may be re-orderedbased on the re-calculated click fractions. In this particular example,a language collection 434 and a click fraction collection 436 illustratethe re-calculated click fractions (e.g., click fraction 438, clickfraction 440 and click fraction 442) for the remaining languages (e.g.,English, German and Spanish).

For each document associated with the query, similar operations may beexecuted by the rank refiner engine 1058. By computing similar data foreach document, quantities may be calculated for scoring and ranking thedocuments. For example, the click data associated with the remaininglanguages for this query/document pair may be summed to provide theclick count for the document. Similar quantities may be calculated foreach document. By summing the click counts for each document (for theremaining languages) a click count may be determined for the query (forthe remaining languages). Similarly, a fraction can be computed for eachdocument, for example, from the ratio of the two sums (e.g., a ratio ofthe sum of click count for the document for the remaining languages andthe sum of the click counts for the query).

One or more techniques may be used to exploit the language compatibilityto define a level. In one arrangement, a level may be defined from thequantity:LD*B+(1−LD)*L+;where B represents the total number amount of click data (e.g.,selections) independent of language and L+ represents the amount ofclick data (e.g., selections) for a specific language or languages afteraccounting for possible languages incompatibilities (e.g., asrepresented in the language collection 434 and the click fractioncollection 436). The quantity LD (referring to as language demotionscore) is a measure of a user's ability to understand the correspondingdocument. For example, LD may have a range from 0 (e.g., represents alow probability that the language of the user is compatible with thelanguage of the document) to 1.0 (e.g., represents a high probabilitythat the language of the user is compatible with the language of thedocument). Once computed, the quantity may be provided to one or morecomponents (e.g., rank modifier engine 1056, ranking engine 1052,indexing engine 1020) and used as a factor in ranking the documentsassociated with the query. One or more weights may also be used with thecomputed quantity for scoring and ranking. For example, click dataassociated with particular countries and country independent click datamay be utilized. Click data associated with particular languages andlanguage independent click data may also be provided to one or morecomponents (individually or in combination with the click dataassociated with one or more countries) for use in determining documentscores and rankings.

While the previous example illustrated in FIG. 4 is associated with aplurality of languages (and associated click fractions) forquery/document pairs, the technique may also be used for othercharacteristics associated with a search requester. For example, ratherthan refining the query/document pair information based upon ofplurality of languages (associated with each query/document pair), clickdata associated with a plurality of countries may be used for refiningquery/document pair information. Further, the rank refiner engine 1058may use the click data (e.g., click fractions) in a similar manner basedupon country compatibility (rather than language compatibility). Assuch, the click data from similar countries, as defined by the rankrefiner engine 1058, may be exploited for scoring and ranking particulardocuments. For example, similar to language compatibility, countrycompatibility a level may be defined by the quantity:L*CD+(1−CD)*C+;where the quantity CD (referring to as country demotion score) is ameasure of a user's ability to understand a document based upon thecountry of the user and the country of the document. Similar to L+, C+is determined by producing and ordered list that includes, for example,click data (e.g., click fractions) of countries that are compatible tothe country of the user. For example, countries in the same generalproximity (e.g., bordering countries such as Mexico and the UnitedStates) may be considered by the rank refiner engine 1058 to becompatible. Countries that share similar cultures, climates, and othercharacteristics may also be considered to be compatible. In thisquantity, L represents the click data associated with the language ofthe user.

In the illustrated example, levels are defined to account forcompatibilities among user languages and locations. As such, theseadditional levels may be used to exploit user interactions (e.g., clickdata) so the ranking of particular search results may be enhanced.Additional types of levels may also be defined for enhancing searchresult ranking, for example, user locations such as regions of acountry, individual cities and metropolitan regions may be used fordefining compatibilities. In one instance, a level may be defined toaccount for the different languages that are used in a single country(associated with a user). For example, a query is identified as beingprovided from an English speaking individual from Germany. Operatingwith a level that is associated with both the language and the countryof the user, a relatively small amount of previous queries (that aresimilar to the current query) may be detected, for this example. Assuch, another level may be activated for identifying similar queries.For example, the level that uses queries from all English speakingindividuals (including individuals from other countries) may be used.However, such an abrupt change in level types may discount queryinformation associated with different user languages (e.g., German) butfrom the same location (e.g., Germany), and which may be pertinent tothe subject query. As such, a level may be defined that uses queries anduser information associated with multiple languages used at a particularcountry. In this example, along with information associated with Englishspeaking users, information (e.g., queries, click data) associated withGerman speaking individuals, French speaking individuals (and otherlanguages used in Germany) are exploited.

One or more techniques and methodologies may be implemented to accountfor user information associated with various languages from a singlecountry. For example, for each document (from a query), the rank refinerengine 1058 may collect click data for each language associated with theuser's country (e.g., for an English speaking user in Germany, clickdata is also collected from Germany speaking individuals, Frenchspeaking individuals, etc.). Once collected, the rank refiner engine1058 may process the click data, for example, one or more weights may beapplied to the click data associated with each language. Constraints mayalso be applied to the weighted or un-weighted click data, for example,the click data may be constrained to one or more limits (e.g., an upperlimit, a lower limit, etc.). The rank refiner engine 1058 may use theprocessed click data to define one or more levels. For example, theprocessed click data may be used in combination with click dataassociated with the language and country of the user to define a level.In one instance, the level may be defined with the quantity:C+LD*C*;where C represents the click data associated with the language andcountry of the user. As used above, the quantity LD refers to languagedemotion score and is a measure of a user's ability to understand thecorresponding document. For example, LD may have a range from 0 (e.g.,representative of a low probability that the language of the user iscompatible with the language of the document) to 1.0 (e.g.,representative of a high probability that the language of the user iscompatible with the language of the document). The quantity C*represents the processed click data for each of the languages associatedwith the country of the user. By applying the language demotion score tothe quantity C*, local documents (e.g., German documents in thisexample) that are relevant may be promoted.

Referring to FIG. 5, a flowchart 500 represents some operations of arank refiner engine (such as the rank refiner engine 1058 shown in FIG.1). The operations may be executed by a single computing device (such asthe server system 1014 shown in FIG. 1) that includes a search engine.In some arrangements, multiple computing devices may also be utilized.Along with being executed at a single site (e.g., server system 1014),operation execution may be distributed among two or more sites (e.g.,server system 1014 and client 1004).

Among other capabilities, the rank refiner engine 1058 may usepredefined compatibilities associated with characteristics of a searchrequester (e.g., location of the search requester, language used, etc.)to refine search results. To provide this functionality, the rankrefiner engine 1058 may execute operations such as receiving 502 searchresults based upon a search query. In one example, for each documentprovided from a search query, click data may be provided to the rankrefiner engine 1058 that is associated with previous queries from userswith similar characteristics (e.g., language used, location, etc.).Operations of the rank refiner engine 1058 may also include ordering 504the characteristics associated with the search results. For example, thelanguages or countries associated with a search results may be orderedbased upon the click data associated with each correspondingcharacteristic. Once ordered, the rank refiner engine 1058 may adjust506 the ordered characteristics based upon one or more predefinedcompatibilities associated with the characteristics. For example, two ormore particular languages may be considered compatible based uponsimilarities of the languages. Similarly, two or more countries may beconsidered compatible based upon similarities (e.g., similar geographiclocation, culture, climate, etc.). In some arrangements, some dataassociated a particular search results (e.g., click data associated withlanguages considered incompatible) may be removed or weighted todecrease its significance. Operations of the rank refiner engine 1058may also include providing 508 the adjusted characteristic informationfor further processing of the search results (e.g., scoring, ranking,etc.). For example, the information may be used to define one or morelevels for processing results such that certain types of search resultsare promoted.

Referring to FIG. 6, a flowchart 600 represents other operations of arank refiner engine (such as the rank refiner engine 1058 shown in FIG.1). Similar to the operations associated with flowchart 500 of FIG. 5,the operations may be executed by a single computing device (such as theserver system 1014 shown in FIG. 1) that includes a search engine. Insome arrangements, multiple computing devices may also be utilized.Along with being executed at a single site (e.g., server system 1014),operation execution may be distributed among two or more sites (e.g.,server system 1014 and client 1004).

The rank refiner engine 1058 may define a level for ranking searchresults that assists promoting local documents identified from thesearch results. For example, click data associated with multiple (orall) languages used at the location of the search requester may beexploited. To provide this functionality, the rank refiner engine 1058may execute operations such as receiving 602 search results that arebased upon a query from a search requestor at a particular location.Operations of the rank refiner engine 1058 may also include identifyinginformation associated with similar queries (e.g., click data) that isassociated with multiple languages used at the location of the searchrequester. Once identified, the rank refiner engine 1058 may processthis information for promoting search results (e.g., documents) that arelocal to the search requester based upon the languages associated withthe location. For example, the rank refiner engine 1058 may combine 606appropriate information (e.g., click data) associated with the multiplelanguages for each search result and provide 608 the information forranking the search results. By using this combination of information,the rank refiner engine 1058 may assist in defining a level of rankingsearch results to promote local search results (e.g., documents) thatmay be more relevant to a search requester than foreign search results.

FIG. 7 is a schematic diagram of an example computer system 700. Thesystem 700 can be used for practicing operations described above. Thesystem 700 can include a processor 710, a memory 720, a storage device730, and input/output devices 740. Each of the components 710, 720, 730,and 740 are interconnected using a system bus 750. The processor 710 iscapable of processing instructions within the system 700. Theseinstructions can implement one or more aspects of the systems,components and techniques described above. In some implementations, theprocessor 710 is a single-threaded processor. In other implementations,the processor 710 is a multi-threaded processor. The processor 710 caninclude multiple processing cores and is capable of processinginstructions stored in the memory 720 or on the storage device 730 todisplay graphical information for a user interface on the input/outputdevice 740.

The memory 720 is a computer readable medium such as volatile or nonvolatile that stores information within the system 700. The memory 720can store processes related to the functionality of the search engine1030 (shown in FIG. 1), for example. The storage device 730 is capableof providing persistent storage for the system 700. The storage device730 can include a floppy disk device, a hard disk device, an opticaldisk device, or a tape device, or other suitable persistent storagemediums. The storage device 730 can store the various databasesdescribed above. The input/output device 740 provides input/outputoperations for the system 700. The input/output device 740 can include akeyboard, a pointing device, and a display unit for displaying graphicaluser interfaces.

The computer system shown in FIG. 10 is but one example. In general,embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing apparatus” encompassesall apparatus, devices, and machines for processing data, including byway of example a programmable processor, a computer, or multipleprocessors or computers. The apparatus can include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them. A propagated signal is anartificially generated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back-end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front-end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. Moreover, the server environment,which is configured to provide electronic search service and employ theranking systems and techniques described, need not be implemented usingtraditional back-end or middleware components. The server environmentcan be implemented using a program installed on a personal computingapparatus and used for electronic search of local files, or the serverenvironment can be implemented using a search appliance installed in anenterprise network.

Other implicit user feedback models can be used in place of thetraditional click fraction model described. For example, an implicituser feedback model employing a large-scale logistic regression modelthat uses the actual query and url as features can be used. The newprior models can be used to denormalize any query-specific click model.

In addition, the prior model(s) can be applied in varying manners. Forexample, a prior model can be applied at run time as an adjustment tothe ranking boost given to a document in accordance with the implicituser feedback model since the set of features used for the prior modelcan be available for direct input at run time. Alternatively, the priormodel can be applied at model building time, where features are fetchedfrom the log(s), which can result in improved response time duringsearches. In addition, when the model is applied at building time, theimplicit feedback can be adjusted per each click record beforeaggregating the feedback from multiple clicks into a signal. Thisadjustment can be for instance a weighting of the clicks according tohow much they were affected by display bias before the clicks areaggregated. At run time, the signal is typically only adjusted after theclicks were already aggregated, which can result in some loss ofprecision.

What is claimed is:
 1. A computer-implemented method comprising:obtaining a plurality of search results responsive to a search querysubmitted by a user, wherein each search result refers to a respectivedocument that is associated with a respective plurality of clickmeasures, each click measure relating to a different respective naturallanguage and representing, at least, a measure of behavior of usersassociated with the respective language in regards to the document whenthe document was referred to in a search result previously provided inresponse to the search query; for each of a first plurality of thesearch results, reducing the click measure associated with the documentreferred to by the search result, wherein the click measure relates to arespective natural language that is incompatible with a natural languageof the user; calculating a respective scoring factor for each of thefirst plurality of search results based on the respective click measuresassociated with the document referred to by the first search result; andranking the search results based upon, at least, the calculated scoringfactors.
 2. The method of claim 1 wherein calculating the scoring factorfurther comprises weighting a combination of the respective clickmeasures for the document by a weight that represents the user's abilityto understand the document.
 3. The method of claim 1 wherein the usersare located in a same country as the user.
 4. The method of claim 1wherein a plurality of the users are located in a different country thanthe user.
 5. The method of claim 4 wherein calculating the scoringfactor further comprises weighting a combination of the respective clickmeasures for the document by a weight that represents a measure of theuser's ability to understand the document based on the country of theuser and a country of the document.
 6. The method of claim 1 whereincalculating the scoring factor further comprises summing the respectiveclick measures for the document.
 7. The method of claim 1 whereincalculating the scoring factor further comprises calculating a ratio ofa sum of the respective click measures for the document and a sum ofclick counts for the query.
 8. The method of claim 1 wherein therespective click measure is a ratio of a number of instances that thedocument was selected for the respective natural language and a numberof instances that document was selected for all of the respectivenatural languages.
 9. The method of claim 1 wherein reducing therespective click measure comprises eliminating the click measure. 10.The method of claim 1, further comprising determining that therespective natural language is incompatible with the natural language ofthe user based on, at least, analysis of search queries submitted byusers in different countries.
 11. A system comprising: one or moreprocessors programmed operable to perform operations comprising:obtaining a plurality of search results responsive to a search querysubmitted by a user, wherein each search result refers to a respectivedocument that is associated with a respective plurality of clickmeasures, each click measure relating to a different respective naturallanguage and representing, at least, a measure of behavior of usersassociated with the respective language in regards to the document whenthe document was referred to in a search result previously provided inresponse to the search query; for each of a first plurality of thesearch results, reducing the click measure associated with the documentreferred to by the search result, wherein the click measure relates to arespective natural language that is incompatible with a natural languageof the user; calculating a respective scoring factor for each of thefirst plurality of search results based on the respective click measuresassociated with the document referred to by the first search result; andranking the search results based upon, at least, the calculated scoringfactors.
 12. The system of claim 11 wherein calculating the scoringfactor further comprises weighting a combination of the respective clickmeasures for the document by a weight that represents the user's abilityto understand the document.
 13. The system of claim 11 wherein the usersare located in a same country as the user.
 14. The system of claim 11wherein a plurality of the users are located in a different country thanthe user.
 15. The system of claim 14 wherein calculating the scoringfactor further comprises weighting a combination of the respective clickmeasures for the document by a weight that represents a measure of theuser's ability to understand the document based on the country of theuser and a country of the document.
 16. The system of claim 11 whereincalculating the scoring factor further comprises summing the respectiveclick measures for the document.
 17. The system of claim 11 whereincalculating the scoring factor further comprises calculating a ratio ofa sum of the respective click measures for the document and a sum ofclick counts for the query.
 18. The system of claim 11 wherein therespective click measure is a ratio of a number of instances that thedocument was selected for the respective natural language and a numberof instances that document was selected for all of the respectivenatural languages.
 19. The system of claim 11 wherein reducing therespective click measure comprises eliminating the click measure. 20.The system of claim 11 wherein the operations further comprisedetermining that the respective natural language is incompatible withthe natural language of the user based on, at least, analysis of searchqueries submitted by users in different countries.
 21. A computerreadable medium having instructions stored thereon that, when executedby one or more processors, causes the one or more processors to performoperations comprising: obtaining a plurality of search resultsresponsive to a search query submitted by a user, wherein each searchresult refers to a respective document that is associated with arespective plurality of click measures, each click measure relating to adifferent respective natural language and representing, at least, ameasure of behavior of users associated with the respective language inregards to the document when the document was referred to in a searchresult previously provided in response to the search query; for each ofa first plurality of the search results, reducing the click measureassociated with the document referred to by the search result, whereinthe click measure relates to a respective natural language that isincompatible with a natural language of the user; calculating arespective scoring factor for each of the first plurality of searchresults based on the respective click measures associated with thedocument referred to by the first search result; and ranking the searchresults based upon, at least, the calculated scoring factors.
 22. Thecomputer readable medium of claim 21 wherein calculating the scoringfactor further comprises weighting a combination of the respective clickmeasures for the document by a weight that represents the user's abilityto understand the document.
 23. The computer readable medium of claim 21wherein the users are located in a same country as the user.
 24. Thecomputer readable medium of claim 21 wherein a plurality of the usersare located in a different country than the user.
 25. The computerreadable medium of claim 24 wherein calculating the scoring factorfurther comprises weighting a combination of the respective clickmeasures for the document by a weight that represents a measure of theuser's ability to understand the document based on the country of theuser and a country of the document.
 26. The computer readable medium ofclaim 21 wherein calculating the scoring factor further comprisessumming the respective click measures for the document.
 27. The computerreadable medium of claim 21 wherein calculating the scoring factorfurther comprises calculating a ratio of a sum of the respective clickmeasures for the document and a sum of click counts for the query. 28.The computer readable medium of claim 21 wherein the respective clickmeasure is a ratio of a number of instances that the document wasselected for the respective natural language and a number of instancesthat document was selected for all of the respective natural languages.29. The computer readable medium of claim 21 wherein reducing therespective click measure comprises eliminating the click measure. 30.The computer readable medium of claim 21 wherein the operations furthercomprise determining that the respective natural language isincompatible with the natural language of the user based on, at least,analysis of search queries submitted by users in different countries.